Immunotherapy outcomes in glioblastoma (GBM) have been limited, prompting questions about the presence and frequency of antigen-specific tumor-infiltrating lymphocytes (TILs) in these tumors. The study reported here sought to identify tumor-specific T cells in IDH-wildtype GBM as a foundation for developing antigen-specific immunotherapies.
The work focuses on the transcriptional states and clonality of intratumoral CD8+ T cells, using single-cell RNA sequencing and T-cell receptor (TCR) sequencing to resolve potentially reactive subsets within tumors that display prominent T-cell infiltration.
Tumor specimens were obtained from 56 patients with newly diagnosed IDH-wildtype GBM. Cases exhibiting prominent intratumoral T-cell infiltration were identified for detailed molecular profiling. From those TIL-rich tumors, CD3+CD8+CD4- T cells were isolated and analyzed by single-cell RNA sequencing paired with TCR sequencing.
The authors integrated their own single-cell dataset from the Aichi Cancer Center (ACC) with publicly available GBM TIL datasets (PA) and with lung cancer TILs and malignant pleural effusion datasets (LC) to contextualize transcriptional programs and clonal architecture across indications.
An AI-based algorithm was applied to identify candidate reactive TCRs and estimate the proportion of potentially tumor-reactive T cells in each dataset.
Integrated analyses compared the ACC TIL-rich subset with the PA GBM datasets and with LC datasets. The study used both transcriptional signatures and TCR clonality to assess whether the TIL-rich GBM samples harbored a higher frequency of potentially reactive T cells relative to other GBM and lung cancer samples.
A machine-learning prediction algorithm was used to nominate TCRs likely to be tumor-reactive. According to the study, this algorithm indicated a markedly higher proportion of predicted reactive T cells in the ACC TIL-rich subset than in the PA and LC datasets.
Unsupervised single-cell analysis identified a dominant cluster within the ACC TIL-rich samples characterized by strong expression of exhaustion-associated genes and high TCR clonality. Among exhaustion markers, LAG3 was most prominently upregulated in this cluster.
Expression of LAG3 and CXCL13 was significantly higher in the ACC TIL-rich subset compared with the PA dataset, suggesting that certain GBM cases contain TILs with distinct exhaustion-related transcriptional profiles. The high TCR clonality observed in the exhaustion-dominant cluster supports the idea of intratumoral clonal expansion of antigen-experienced T cells within these GBM cases.
The association between transcriptional profiles and clonality was further explored to link expanded T-cell clones with specific functional states, notably exhaustion-related programs.
The authors conclude that some GBM cases with marked clonal expansion among intratumoral CD8+ T cells may include potentially tumor-reactive cells despite exhibiting exhaustion signatures. These findings provide a rationale for exploring antigen-specific immunotherapy approaches in GBM as well as therapies targeting exhaustion pathways, including LAG-3 blockade.
The study positions clonally expanded, exhausted CD8+ TILs as candidate reservoirs of tumor reactivity that could be harnessed or reinvigorated by targeted immunotherapeutic strategies.
The article includes several figures that visualize the main findings:
Figure 1: Identification of GBM cases with abundant TILs.
Figure 2: Single-cell analysis of CD8+ TILs from two TIL-rich GBM cases.
Figure 3: Integrated single-cell analysis of CD8+ TILs from the ACC-2S and PA datasets.
Figure 4: Association between TCR clonality and transcriptional profiles of GBM-infiltrating T cells.
Figure 5: Integrated analysis of GBM TILs from the ACC-2S dataset and LC samples.
Figure 6: Distribution of predicted tumor-reactive T cells across GBM and LC datasets.
Each figure supports the core observations about TIL abundance, exhaustion marker expression (notably LAG3 and CXCL13), clonal expansion, and the predicted distribution of reactive T cells across datasets.
The datasets generated and/or analyzed during the study are available from the corresponding authors upon reasonable request. Custom R scripts used for statistical analyses, data visualization, and generation of key figures are likewise available from the corresponding authors upon reasonable request. The Materials and Methods section of the article describes the software packages and versions used for analysis.
The article includes an extensive reference list situating the work within the glioma and immunotherapy literature. Representative cited topics include standard-of-care glioma references, clinical immunotherapy trials in GBM, and recent CAR T-cell efforts in central nervous system tumors.
Note: This summary is restricted to facts and findings reported in the source article preview and abstract. Details beyond those presented in the source, including full methodological parameters, numeric results, and supplementary analyses, were not reported in the provided text and are available in the full article.